Abstract: A computer-implemented method includes inputting, to a processor, genomic data from a plurality of subjects, the genomic data including first sample genomic data prior to a treatment, and second sample genomic data after the treatment; determining, by the processor, a plurality of ?'s for the plurality of subjects, wherein each ? is a genetic change in the second sample compared to the first sample genomic data; creating, by the processor, a matrix of the plurality of subjects and their features which features are the genetic changes or clusters of genetic changes in the plurality of ?'s of the subjects; biclustering, by the processor, the matrix of the plurality of subjects and their features, to provide clumps of subjects sharing a common feature such as a shared genetic change or shared cluster of genetic changes; and outputting, by the processor, the clumps of subjects, the common features, and the treatment.

Abstract: A method of electronically separating host and non-host sequence data (sequenced DNA, RNA, and/or proteins) utilizes electronic host filters, which can be generated on a just-in-time basis by a cloud-based software service. Host reads and non-host reads of a given sample are separated and stored in separate data repositories. Also disclosed is a cloud-based software service utilizing the method. The non-host reads resulting from the host filtration process can then be profiled more accurately for the microorganism content therein.

Abstract: Embodiments of the present invention are directed to methods for adapting machine learning, redescription, and computational homology techniques to the identification of pathogenic pathways. A non-limiting example of the computer-implemented method includes receiving genetic and biological data and generating a data matrix based on the data. The data matrix can include one or more features, and each feature can be associated with a known feature value. A collection of sets of features representing pathways, genes, or a genetic combination of genotype values can be determined. The method also includes determining a first prediction for a feature value of a selected feature to be predicted in the collection, permuting one or more rows of the data matrix, and recalculating a second prediction for the feature value based on the permutation. A prediction score can be determined based on the first prediction, the second prediction, and a known feature value.

Abstract: A computer-implemented method includes inputting, to a processor, an N×K SSV frequency matrix M and an error tolerance ??0, wherein N is a number of SSVs and K is a number of time points, wherein matrix M comprises a plurality of time-resolved mutation frequencies for each SSV; clustering, by the processor, matrix rows in M that satisfy the ? to provide a plurality of SSV clusters; assigning, by the processor, a mean cluster frequency to each SSV within each SSV cluster; calculating errors for removing low frequency rows, for rounding rows to 1 or 0; assigning a root node for all SSV clusters of frequency 1; and calculating, by the processor, a ?-compliant time-series evolution tree with error ?? comprising the root node and a plurality time-stratified nodes, wherein calculating includes assigning a clonal configuration, optionally re-configuring the clonal configuration, and calculating error for re-configuring.

Abstract: A computer-implemented method incudes calculating, by a processor, based on sequence data for a tumor from a subject at a plurality of time points, a mutation frequency for each of a plurality of SSVs at each of the time points to provide a plurality of time-resolved mutation frequencies (between 0 and 1) for each of the plurality of SSVs, the sequence data including a plurality of simple somatic variations (SSVs) at each of the time points; binning, by the processor, the plurality of time-resolved mutation frequencies for each SSV at each of the time points to provide a matrix of SSVs and time points; converting, by the processor, the matrix cells to pseudo-clones; and constructing, by the processor, a time-series tumor evolution tree from the pseudo-clones, wherein each time point in the time-series evolution tree represents an event in the subject's cancer treatment.

Abstract: A computer-implemented method includes determining, by a processor, from a time-series evolution tree comprising one or more clones at each of the plurality of time points, that the one or more clones are sensitive clones or resistant clones, wherein the time-series evolution tree is based on sequence data for a tumor from a subject at a plurality of time points, wherein each time point in the time-series evolution tree represents an event in the subject's cancer treatment, and wherein a clone is a collection of gene alterations; based at least in part on determining that the one or more clones that are the sensitive or resistant clones, determining, by the processor, a geneset composition of the one or more clones that are the sensitive or resistant clones; and based at least in part on determining the geneset composition, determining by the processor, a further treatment for the subject.

Abstract: A computer-implemented method includes generating, by a processor, a set of training data for each phenotype in a database including a set of subjects. The set of training data is generated by dividing genomic information of N subjects selected with or without repetition into windows, computing a distribution of genomic events in the windows for each of N subjects, and extracting, for each window, a tensor that represents the distribution of genomic events for each of N subjects. A set of test data is generated for each phenotype in the database, a distribution of genomic events in windows for each phenotype is computed, and a tensor is extracted for each window that represents a distribution of genomic events for each phenotype. The method includes classifying each phenotype of the test data with a classifier, and assigning a phenotype to a patient.

Abstract: Methods and systems for genetic diagnosis include splitting genomes into respective groups of non-overlapping windows. The genomes are sampled into sets, each set being made up of selected genomes. A distribution of events is generated across the sets in each window. A tensor is determined for each window based on statistical properties of the distribution of events for the window. A classifier is generated based on the tensors. One or more phenotypes is diagnosed from an input genome using the classifier.

Abstract: Embodiments include methods, systems, and computer program products for analyzing genomic data. Aspects include receiving genomic data for an organism, sample phenotypes, and a plurality of gene sets. Aspects include, for each of the gene sets, determining a set of genes G corresponding to genes in the gene set and a set of genes G? corresponding to genes outside the gene set for the phenotypes R and R?. Aspects also include determining a set of mutated genes M and a set of non-mutated genes M? for R and R? and a mutation enrichment score. Aspects also include determining a set of differentiated genes D a set of non-differentiated genes D? for R and R?. Aspects also include identifying an enriched gene set GE based at least in part upon the mutation enrichment score and the differentiation enrichment score.

Abstract: A computer-implemented method, computer program product, and computer processing system are provided for metagenomic pattern classification. The method includes pre-processing, by a processor, a taxonomy tree associated with a genome database to extract taxonomy related information therefrom. The genome database includes a plurality of genome sequences. The method further includes building, by the processor, a suffix tree on the genome database. The method also includes annotating, by the processor, nodes in the suffix tree, using a plurality of right maximal patterns derived from the extracted taxonomy related information as annotations, such that each of the plurality of right maximal patterns in the suffix tree points to a respective one of a plurality of nodes in the taxonomy tree and such that a leaf node in the taxonomy tree represents a respective sample organism. The annotations are configured to function as classifications for the plurality of genome sequences.

Abstract: A computer-implemented method, computer program product, and computer processing system are provided for phasing polyploids. The method includes receiving, by a processor, a n×m matrix including a set of n rows and a set of m columns. Each of the n rows represents a respective one of n samples. Each of the m columns represents a respective one of m SNPs for two or more sample organisms. The method further includes representing, by the processor, each allele in the m SNPs as a binary number. The method also includes phasing, by the processor, the n samples to determine a haplotype of a parent of the two or more sample organisms. The phasing is performed using a trie to process a distribution of alleles in the n-samples that includes homozygous alleles and heterozygous alleles.

Abstract: A computer-implemented method is provided for phasing polyploids. The method includes assembling, by a processor, haplotypes in polyploid genomes of two or more sample organisms. The method further includes performing, by the processor, haplotype phasing on the haplotypes using a haplotype phasing process having multiple phases applied to ploid samples corresponding to the haplotypes to determine which parent chromosomes possess which of the haplotypes. The step of performing haplotype phasing includes (i) reducing, in each of the multiple phases, a ploidy p to a floor value (p/2), (ii) assigning variables in at least some of the multiple phases such that resulting intermediate ones of the ploid samples co-occur multiple times in the at least some of the multiple phases, and (iii) linking, in each of the multiple phases, the ploid samples together using one or more daisy chain models and daisy chain linkage criteria.

Abstract: Embodiments include method, systems and computer program products for metagenome mapping. Aspects include receiving a plurality of operational taxonomic unit (OTU) identifications from a sample. Aspects also include calculating rank distributions for OTU identifications, ranking OTU identifications, and retaining or discarding OTU identifications based on the rankings. Aspects also include calculating a promiscuity score for each of the operational taxonomic unit identifications, wherein the promiscuity score is a number that reflects a likelihood of a false positive. Aspects also include ranking or discarding OTU identifications based on the rankings.

Abstract: Discloses are a method of and a system for identifying a motif in a graph. The graph has multiple vertices, and the vertices have one or more attributes. The method comprises the steps of, for each of the vertices that have at least a defined one attribute, identifying a set of vertices, if any, adjacent to said each vertex and having at least one specified attribute; and forming a first list comprised of said identified sets. The method comprises the further steps of determining the unique intersections of the sets of said first list; computing compact forms of the sets on said first list; and identifying a motif of the graph from said unique intersections.

Abstract: Disclosed are a method of and system for clustering objects and finding prime redescriptors for the clusters of objects. The method comprises the step of forming a matrix, including (i) identifying on the matrix, each of a set of given objects, and (ii) for each of said set of objects, identifying on the matrix, by using binary values, whether or not the object has each of a set of given features. The method comprises the further steps of finding all the minimal pure disjunctions on the matrix, adding said minimal pure disjunctions to the matrix to form an augmented matrix, and finding all the maximal pure conjunctions on the augmented matrix. These maximal pure conjunctions are used to identify prime redescriptors for the set of objects.

Abstract: Basis motifs awe determined from an input sequence through an iterative technique that begins by creating small solid motifs and continues to create larger motifs that include “don't care” characters and that can include flexible portions. The small solid motifs, including don't care character's and flexible portions, are concatenated to create larger motifs. During each iteration, motifs are trimmed to remove redundant motifs and other motifs that do not meet certain criteria. The process is continued until no new motifs are determined. At this point, the basis set of motifs has been determined. The basis motifs are used to construct redundant motifs. The redundant motifs axe formed by determining a number of sets for selected basis motifs. From these sets, unique intersection sets are determined. The redundant motifs are determined from the unique intersection sets and the basis motifs.

Abstract: A method (and structure) of data processing in which data is represented in a lossy data format as a plurality of extensible motifs. Each extensible motif has at least one don't-care character enclosed by at least one non-don't-care character on a left side and at least one non-don't-care character on a right side.

Abstract: Discloses are a method of and a system for identifying a motif in a graph. The graph has multiple vertices, and the vertices have one or more attributes. The method comprises the steps of, for each of the vertices that have at least a defined one attribute, identifying a set of vertices, if any, adjacent to said each vertex and having at least one specified attribute; and forming a first list comprised of said identified sets. The method comprises the further steps of determining the unique intersections of the sets of said first list; computing compact forms of the sets on said first list; and identifying a motif of the graph from said unique intersections.

Abstract: A method for discovering a fuzzy bi-cluster is disclosed. The method includes reading a matrix comprising rows and columns and reading at least one input parameter specifying a fuzzy bi-cluster. The method further includes discovering in the matrix at least one fuzzy bi-cluster that was specified and storing the at least one fuzzy bi-cluster that was discovered.

Abstract: A method (and system) of analyzing protein folding trajectory includes a combinatorial pattern discovery process for analyzing multidimensional data from a simulation of the protein folding trajectory. The method of analyzing protein folding trajectory allows a user to understand the global state changes and folding mechanism of a protein during its folding process. The method may further include generating a collection of data points by simulation experiments, analyzing the collection of data points to extract patterned clusters, filtering the patterned clusters to remove insignificant patterned clusters to obtain a collection of filtered patterned clusters and analyzing the collection of filtered pattern clusters.